Summary of Pretrained Visual Uncertainties, by Michael Kirchhof and Mark Collier and Seong Joon Oh and Enkelejda Kasneci
Pretrained Visual Uncertainties
by Michael Kirchhof, Mark Collier, Seong Joon Oh, Enkelejda Kasneci
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces the first-ever pretrained uncertainty modules for vision models, enabling zero-shot transfer of uncertainties learned on a large pretraining dataset to specialized downstream datasets. The innovation lies in solving a gradient conflict issue and accelerating training by up to 180x on ImageNet-21k. The trained uncertainties generalize well to unseen datasets, capturing aleatoric uncertainty and disentangling it from epistemic components. This breakthrough enables safe retrieval and uncertainty-aware dataset visualization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine learning more trustworthy by creating a way to estimate uncertainty that can be used for many different tasks without needing to start from scratch. It does this by training a model on a large dataset, then using that training to help with other tasks. This makes it easier and safer to use models in real-world situations. |
Keywords
* Artificial intelligence * Machine learning * Pretraining * Zero shot
